1. Field of the Invention
This invention relates to object tracking in video devices, more particularly for an automated process and apparatus to track objects in video images.
2. Background of the Invention
Video devices that capture or display video images use object tracking for several purposes. In one example, a video device automatically focuses the picture on a specific object. In another example, the device automatically zooms in on that object. Other purposes include correction of brightness or contrast of the object or highlighting the object in a display.
Current methods typically use one of three types of algorithms: 1) correlation between adjacent frames; 2) geometrical object models; or, 3) illumination models. Correlation techniques typically use either a dot product or sum of squared differences (SSD) analysis to compare images. A portion of the initial frame is compared against several candidate regions in the next frame. Either technique produces a score for each candidate region of the next frame compared to the initial frame. The highest score indicates the location of the object in the next frame.
The second type of algorithm, geometric techniques, uses geometric modeling of objects in the image. A three-dimensional model of an object, such as a face, is created and projected onto a number of two-dimensional planes corresponding to every possible angle of orientation of the object. An input image is then compared against each possible two-dimensional projection until a match is found. This technique is computationally expensive and does not contemplate other objects that can partially occlude the tracked object, such as if it moved behind something in the next frame.
A third current method of tracking objects uses illumination modeling. All kinds of illumination and all possible condition changes are developed, then used in analyzing the next image. This method can be exhaustive, although techniques are available to reduce the load. Even with these techniques, the illumination approach has a disadvantage if the illumination changes in a way that is not predicted, making the tracking inaccurate. Also, problems occur if the object being projected alters its orientation, which will change the illumination model of the object, and partial occlusion remains a problem for this technique as well.
In summary, these current techniques have one or more disadvantages. These include sensitivity to object orientation and/or scale changes, sensitivity to partial occlusions and shadows, sensitivity to camera automatic exposure and lighting condition changes, or they are computationally intensive. Higher computation intensity results in slow response time, making real-time processing problematic.
Therefore, a method and apparatus are needed that track objects quickly with a reduced sensitivity to these problems.